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All Journal JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Intechno Journal : Information Technology Journal
Vikri Aulia
Universitas Negeri Padang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Mathematics

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Pengembangan Sistem Validasi Dokumen Pasca Perceraian Menggunakan Optical Character Recognition dan Relationship-Based Access Control Erik Rahman; Resmi Darni; Dony Novaliendry; Vikri Aulia
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 8 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v8i1.17113

Abstract

This study aims to address the problem of post-divorce population document validation, which is still carried out manually and is prone to errors and delays. The proposed approach is the development of a document validation system using Optical Character Recognition (OCR) for data extraction and Relationship-Based Access Control (ReBAC) for relationship-based access management. The research dataset consisted of 122 identity documents in JPG, PNG, and PDF formats that had undergone anonymization and preprocessing stages, including grayscale conversion and noise removal. The testing results showed that the OCR system achieved matching rates of 74.59% for the National Identification Number (NIK) attribute, 89.34% for the name attribute, and 75.41% for the address attribute, with an average match score of 84.65%. In addition, ReBAC testing demonstrated a 100% success rate across 32 testing scenarios in preventing unauthorized access based on inter-entity relationships. These results indicate that the developed system is capable of improving efficiency, accuracy, and security in the post-divorce population document validation process.
Flow-Based Encrypted Network Traffic Classification Using Random Forest for Network Access Control Wahyu Isnan; Yulia Fatmi; Resmi Darni; Vikri Aulia
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2922

Abstract

Encrypted network communications reduce the effectiveness of payload based traffic identification and complicate the translation of traffic analysis into network access control decisions. This study evaluates a payload independent workflow that connects flow based multiclass classification with administrator triggered, time limited firewall enforcement. The experiment used the public ISCXVPN2016 benchmark. After removing 18,719 duplicate records, 40,987 unique flows remained; all 23 available numerical flow features were retained without feature selection or normalization. A Random Forest classifier with 300 trees was selected using five-fold cross-validated grid search on a stratified 80% training partition and evaluated on an independent 8,198 sample test set covering 14 VPN and non-VPN traffic classes. The model achieved 88.01% accuracy, 88.00% weighted precision, 88.01% weighted recall, and 87.97% weighted F1-score. It exceeded the strongest reproduced baseline, K-Nearest Neighbors, by 16.09 percentage points in accuracy and 16.29 percentage points in weighted F1-score. Supplemental five-fold evaluation produced a mean accuracy of 88.05% with a 0.36 percentage point standard deviation. The trained classifier was integrated with a web application in which administrators review predicted flows and initiate temporary MikroTik RouterOS rules. All 30 temporary blocking entries observed in the evaluation database reached the Unblocked state with recorded timestamps, demonstrating rule lifecycle traceability at the database level. The findings show that Random Forest can provide competitive flow based classification while supporting an auditable, human controlled access control workflow; however, device level reliability and cross dataset generalizability require further validation.
Co-Authors Dony Novaliendry Erik Rahman Resmi Darni Wahyu Isnan Yulia Fatmi